Zobrazeno 1 - 10
of 977
pro vyhledávání: '"Xu, Yongjun"'
Modeling trajectory data with generic-purpose dense representations has become a prevalent paradigm for various downstream applications, such as trajectory classification, travel time estimation and similarity computation. However, existing methods t
Externí odkaz:
http://arxiv.org/abs/2410.13196
Although the diffusion model has achieved remarkable performance in the field of image generation, its high inference delay hinders its wide application in edge devices with scarce computing resources. Therefore, many training-free sampling methods h
Externí odkaz:
http://arxiv.org/abs/2410.07679
Continual learning (CL) is designed to learn new tasks while preserving existing knowledge. Replaying samples from earlier tasks has proven to be an effective method to mitigate the forgetting of previously acquired knowledge. However, the current re
Externí odkaz:
http://arxiv.org/abs/2410.06645
Autor:
Fu, Yisong, Wang, Fei, Shao, Zezhi, Yu, Chengqing, Li, Yujie, Chen, Zhao, An, Zhulin, Xu, Yongjun
Recently, Transformers have gained traction in weather forecasting for their capability to capture long-term spatial-temporal correlations. However, their complex architectures result in large parameter counts and extended training times, limiting th
Externí odkaz:
http://arxiv.org/abs/2408.09695
Autor:
Zhang, Yuting, Wu, Yiqing, Han, Ruidong, Sun, Ying, Zhu, Yongchun, Li, Xiang, Lin, Wei, Zhuang, Fuzhen, An, Zhulin, Xu, Yongjun
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging i
Externí odkaz:
http://arxiv.org/abs/2407.00912
Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a student agent. However, the teacher-student framework requires a well-t
Externí odkaz:
http://arxiv.org/abs/2406.05488
Existing Incremental Object Detection (IOD) methods partially alleviate catastrophic forgetting when incrementally detecting new objects in real-world scenarios. However, many of these methods rely on the assumption that unlabeled old-class objects m
Externí odkaz:
http://arxiv.org/abs/2406.04829
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on cru
Externí odkaz:
http://arxiv.org/abs/2405.16420
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhang, Xu, Zhuang, Fuzhen, Lin, Leyu, Kang, Zhanhui, Xu, Yongjun
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike,
Externí odkaz:
http://arxiv.org/abs/2405.15280